Out-of-Distribution (OoD) Detection capabilities are critical for identifying distributional shifts, outliers and irregular user inputs, which can hinder the propagation of erroneous decision in an automated system. We evaluate OoD performance on the commonly used benchmarks [1]}, where we use FashionMnist [2]} as in-distribution and Mnist [3]} as OoD.
fig:roccurves (c) shows that our online lae outperforms existing models in both log-likelihood and Typicality score [4]}. This stems from the calibrated model uncertainties, which are exemplified in the models ability to detect OoD examples from the uncertainty deduced in latent and output space; see fig:roccurves (a,b) for roc curves.
